New quantum circuit architecture for classifying big data

This research is motivated by the incomplete use of quantum in existing learning algorithms, so that the proposed learning algorithm is not optimal. Research (Fahri & Neven, 2018) shows that the proposed method of architectural form still uses classical architecture but inputs, weights and targets already use a quantum approach. Based on the results of previous studies, it shows that quantum computing is better than classical computation. Many researchers use quantum computing in the proposed learning algorithm. The model proposed is a quantum circuit architecture with the quantum perceptron method consisting of a quantum bit gate that uses a quantum computational approach as the architecture of the quantum perceptron learning algorithm. Then the authors conduct training and testing of the proposed quantum circuit architecture to test the quantum circuit model that the author proposes. The result of this research is a quantum circuit model with the quantum perceptron method which can be used to solve the learning optimization problem by using a quantum circuit architecture with 5 measurement measurements to show error training and testing = 0, with 9 measurements showing an error training of 1.13%, error testing 2.06%.

[1]  S. Lloyd,et al.  Quantum algorithms for supervised and unsupervised machine learning , 2013, 1307.0411.

[2]  Maria Schuld,et al.  The quest for a Quantum Neural Network , 2014, Quantum Information Processing.

[3]  Masoud Mohseni,et al.  Quantum support vector machine for big feature and big data classification , 2013, Physical review letters.

[4]  S. Lloyd,et al.  Quantum principal component analysis , 2013, Nature Physics.

[5]  Elizabeth Behrman,et al.  Efficient learning algorithm for quantum perceptron unitary weights , 2015 .

[6]  Ammar Daskin,et al.  A Quantum Implementation Model for Artificial Neural Networks , 2016, ArXiv.

[7]  Teresa Bernarda Ludermir,et al.  Quantum perceptron over a field and neural network architecture selection in a quantum computer , 2016, Neural Networks.

[8]  Teresa Bernarda Ludermir,et al.  Weightless neural network parameters and architecture selection in a quantum computer , 2016, Neurocomputing.

[9]  Roger Melko,et al.  Quantum Boltzmann Machine , 2016, 1601.02036.

[10]  Robert Gardner,et al.  Quantum generalisation of feedforward neural networks , 2016, npj Quantum Information.

[11]  Ashish Kapoor,et al.  Quantum Perceptron Models , 2016, NIPS.

[12]  Carlos Pedro Gonçalves.  Quantum Neural Machine Learning - Backpropagation and Dynamics , 2016, ArXiv.

[13]  Yuan Feng,et al.  Quantum Privacy-Preserving Perceptron , 2017, ArXiv.

[14]  Nathan Wiebe,et al.  Can small quantum systems learn , 2015, Quantum Inf. Comput..

[15]  S. Lloyd,et al.  Quantum Hopfield neural network , 2017, Physical Review A.

[16]  Hartmut Neven,et al.  Classification with Quantum Neural Networks on Near Term Processors , 2018, 1802.06002.

[17]  Shinichiro Taguchi,et al.  Optimization of neural networks via finite-value quantum fluctuations , 2018, Scientific Reports.

[18]  R. Wu,et al.  Quantum Circuit Design for Training Perceptron Models , 2018, 1802.05428.

[19]  R. V. Meer,et al.  Efficient evaluation of electron correlation along the bond-dissociation coordinate in the ground and excited ionic states with dynamic correlation suppression and enhancement functions of the on-top pair density , 2018, Physical Review A.

[20]  T. Akinci,et al.  Decoherence in a Quantum Neural Network , 2018, 1806.07251.

[21]  Ammar Daskin,et al.  A Simple Quantum Neural Net with a Periodic Activation Function , 2018, 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[22]  E. Torrontegui,et al.  Universal quantum perceptron as efficient unitary approximators , 2018 .

[23]  Seth Lloyd,et al.  Continuous-variable quantum neural networks , 2018, Physical Review Research.

[24]  Muharman Lubis,et al.  Big Data Forecasting Applied Nearest Neighbor Method , 2019, 2019 International Conference on Sustainable Engineering and Creative Computing (ICSECC).